Breast cancer screening policies attempt to achieve timely diagnosis by regularly screening healthy women via various imaging tests. Various clinical decisions are needed to manage the screening process: selecting initial screening tests, interpreting test results, and deciding if further diagnostic tests are required. Current screening policies are guided by clinical practice guidelines (CPGs), which represent a 'one-size-fits-all' approach, designed to work well (on average) for a population, and can only offer coarse expert-based patient stratification that is not rigorously validated through data. Since the risks and benefits of screening tests are functions of each patient's features, personalized screening policies tailored to the features of individuals are desirable. To address this issue, we developed ConfidentCare: a computer-aided clinical decision support system that learns a personalized screening policy from electronic health record (EHR) data. By a 'personalized screening policy,' we mean a clustering of women's features, and a set of customized screening guidelines for each cluster. ConfidentCare operates by computing clusters of patients with similar features, then learning the 'best' screening procedure for each cluster using a supervised learning algorithm. The algorithm ensures that the learned screening policy satisfies a predefined accuracy requirement with a high level of confidence for every cluster. By applying ConfidentCare to real-world data, we show that it outperforms the current CPGs in terms of cost efficiency and false positive rates: a reduction of 31% in the false positive rate can be achieved.

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.